Here we present graphs of the results of the Toxosources consumer survey. Raw data files are available at…

library( RColorBrewer )
library( tidyverse )
library( haven )
library( here )


setwd(here("consumption", "GFK survey"))
source( "functions.R" )

df_survey_info    <- read_survey_info()
df_survey_freq    <- read_survey_freq()
df_survey_process <- read_survey_process()
df_survey_type    <- read_survey_type()

my_theme <-     theme( panel.background = element_blank(),
                       panel.grid.major = element_blank(),
                       strip.text = element_text( size=15 ),
                       axis.title = element_text( size=15 ),
                       axis.text = element_text( size=15 ),
                       plot.title = element_text( size=25 ),
                       legend.title = element_text( size=15 ),
                       legend.text = element_text( size=15 ),
                       plot.margin = margin(0, 1, 0, 0, "cm"),
                       legend.position="bottom" )

1 Consumption Frequencies

1.1 Meat in general

This category includes all meat, including meat which is heated thoroughly and therefore microbiologically safe.

df_survey_process %>%
  dplyr::select( freq_meat_general, id ) %>% 
  left_join( df_survey_info, by="id" ) %>% 
  calc_perc( freq_meat_general ) %>% 
  plot_freq( "Consumption frequency of meat", freq_meat_general )

#ggsave("figures/freq_meat_general.png", width = 35, height = 20, units="cm", dpi=300 )

1.2 Undercooked meat consumption frequency

freq_meat_labels = c( 
   "freq_meat_beef_veal"="Veal",
   "freq_meat_pork"="Pork",
   "freq_meat_poultry"="Poultry",
   "freq_meat_lamb"="Lamb",
   "freq_meat_mutton"="Mutton",
   "freq_meat_horse"="Horse",
   "freq_meat_other"="Other",
   "freq_meat_game"="Game",         
   "freq_meat_wild_birds"="Birds",
   "freq_meat_mince_beef"="Beef",
   "freq_meat_mince_pork"="Pork",
   "freq_meat_mince_mix"="Mix",    
   "freq_meat_mince_misc"="Misc",
   "freq_meat_sausage"="Sausage",
   "freq_meat_spread"="Spread",
   "freq_meat_cured_pork"="Cured Pork",   
   "freq_meat_cured_beef"="Cured Beef",
   "freq_meat_cured_misc"="Cured Misc",
   "freq_meat_dry_fermented"="Dry Fermented",
   "freq_meat_dried"= "Dried Meat",
   "freq_meat_carpaccio"="Carpaccio",
   "freq_meat_tartare"= "Tartare" )      

df_survey_freq %>%
  dplyr::select( starts_with("freq_meat"), id ) %>% 
  left_join( df_survey_info, by="id" ) %>% 
  pivot_longer( cols=starts_with( "freq_meat"), names_to = "meat", values_to = "consumed" ) %>% 
  calc_perc( meat, consumed ) %>% 
  plot_freq( "Frequency of consumption of meat products",
             fill_by=consumed, wrap_by=meat, my_labels=freq_meat_labels )+ 
  guides(fill = guide_legend(nrow = 3, byrow = T))

#ggsave("figures/freq_meat.png", width = 35, height = 25, units="cm", dpi=300)

1.3 Raw meat consumption frequency

freq_labels_raw= c( "freq_raw_shell"     = "Shellfish",
                    "freq_raw_meatball"  = "Meatball",
                    "freq_raw_milk"      = "Raw Milk" )

df_survey_freq %>%  #
  dplyr::select( starts_with("freq_raw"), id ) %>% 
  left_join( df_survey_info, by="id" ) %>% 
  pivot_longer( cols=starts_with( "freq_raw"), names_to = "raw", values_to = "consumed" ) %>% 
  calc_perc( raw, consumed ) %>% 
  plot_freq( "Frequency of consumption of raw products",
             fill_by=consumed, wrap_by=raw, my_labels=freq_labels_raw )+ 
  guides(fill = guide_legend(nrow = 3, byrow = T))

#ggsave("figures/freq_raw.png", width = 35, height = 25, units="cm", dpi=300)

1.4 Fruit and Vegetables Consumption Frequencies

freq_veg_labels = c("freq_veg_berries"    = "Berries",
                    "freq_veg_pome_fr"    = "Pomme Fruit",
                    "freq_veg_brassic"    = "Brassica",
                    "freq_veg_cucurbi"    = "Cucurbi",
                    "freq_veg_tomato"     = "Tomato",    
                    "freq_veg_peppers"    = "Peppers",
                    "freq_veg_fungi"      = "Fungi",
                    "freq_veg_herbs"      = "Herbs",
                    "freq_veg_leafy_veg"  = "Leafy Vegetables",
                    "freq_veg_sprouts"    = "Sprouts",
                    "freq_veg_leafy_misc" = "Leafy Misc", 
                    "freq_veg_roots"      = "Roots", 
                    "freq_veg_stems"      = "Stems", 
                    "freq_veg_fermented"  = "Fermented", 
                    "freq_veg_asparagus"  = "Asperagus",
                    "freq_veg_sugarsnaps" = "Sugarsnaps",
                    "freq_veg_ginger"     = "Ginger",
                    "freq_veg_salicornia" = "Salicornia" )

df_survey_freq %>%  
  dplyr::select( starts_with("freq_veg"), id ) %>%
  left_join( df_survey_info, by="id" ) %>% 
  pivot_longer( cols=starts_with( "freq_veg"), names_to = "veg", values_to = "consumed" ) %>%
  calc_perc( veg, consumed ) %>% 
  plot_freq( "Frequency of consumption of vegetables",
           fill_by=consumed, wrap_by=veg, my_labels=freq_veg_labels )

#ggsave("figures/freq_veg.png", width = 15, height = 15)

1.5 Country specialties

1.5.1 Czech Republic

cz_labels= c( "freq_cz_tatarak_divocaka" = "Tatarak divocaka",
              "freq_cz_pecene_veprove"   = "Pecene veprove",
              "freq_cz_veprovy_mozecek"  = "Veprovy mozecek",
              "freq_cz_jitrnice"         = "Jitrnice",       
              "freq_cz_jelito"           = "Jelito",
              "freq_cz_tlacenka"         = "Tlacenka" )

df_survey_freq %>%
  dplyr::select( starts_with("freq_cz"), id ) %>% 
  left_join( df_survey_info, by="id" ) %>% 
  filter( country == "Czech Republic" ) %>%
  pivot_longer( cols=starts_with( "freq_cz"), names_to = "specialty", values_to = "consumed" ) %>% 
  calc_perc( specialty, consumed ) %>% 
  plot_freq_specialty( "Frequency of consumption of Czech Republic specialty products",
             fill_by=consumed, my_labels=cz_labels )

#ggsave("figures/freq_cz_specialty.png", width = 35, height = 25, units="cm", dpi=300)

1.5.2 Germany

de_labels= c( "freq_de_hackepeter" = "hackepeter",
              "freq_de_knackwurst" = "knackwurst" )
  
df_survey_freq %>%
  dplyr::select( starts_with("freq_de"), id ) %>% 
  left_join( df_survey_info, by="id" ) %>% 
  filter( country == "Germany" ) %>%
  pivot_longer( cols=starts_with( "freq_de"), names_to = "specialty", values_to = "consumed" ) %>% 
  calc_perc( specialty, consumed ) %>% 
  plot_freq_specialty( "Frequency of consumption of German specialty products",
                       fill_by=consumed, my_labels=de_labels )

#ggsave("figures/freq_de_specialty.png", width = 35, height = 25, units="cm", dpi=300)

1.5.3 Denmark

dk_labels <- c( "freq_dk_medisterpolse"     = "Medisterpolse",
                "freq_dk_rullepolse"        = "Rullepolse",
                "freq_dk_hjerter_flodesovs" = "Hjerter flodesovs" )

df_survey_freq %>%
  dplyr::select( starts_with("freq_dk"), id ) %>% 
  left_join( df_survey_info, by="id" ) %>% 
  filter( country == "Denmark" ) %>%
  pivot_longer( cols=starts_with( "freq_dk"), names_to = "specialty", values_to = "consumed" ) %>% 
  calc_perc( specialty, consumed ) %>% 
  plot_freq_specialty( "Frequency of consumption of Danish specialty products",
                       fill_by=consumed, my_labels=dk_labels )

#ggsave("figures/freq_dk_specialty.png", width = 35, height = 25, units="cm", dpi=300)

1.5.4 France

fr_labels <- c( "freq_fr_figatelle"    = "Figatelle",
                "freq_fr_mettwurst"    = "Mettwurst",
                "freq_fr_museau_porc"  = "Museau Porc",
                "freq_fr_museau_boeuf" = "Museau Boeuf" )

df_survey_freq %>%
  dplyr::select( starts_with("freq_fr"), id ) %>% 
  left_join( df_survey_info, by="id" ) %>% 
  filter( country == "France" ) %>%
  pivot_longer( cols=starts_with( "freq_fr"), names_to = "specialty", values_to = "consumed" ) %>% 
  calc_perc( specialty, consumed ) %>% 
  plot_freq_specialty( "Frequency of consumption of French specialty products",
                       fill_by=consumed, my_labels=fr_labels )

#ggsave("figures/freq_fr_specialty.png", width = 35, height = 25, units="cm", dpi=300)

1.5.5 Netherlands

nl_labels <- c( "freq_nl_filet_americain"  = "Filet Americain",
                "freq_nl_paardenrookvlees" = "Paardenrookvlees",
                "freq_nl_ossenworst"       = "Ossenworst", 
                "freq_nl_theeworst"        = "Theeworst",
                "freq_nl_rosbief"          = "Rosbief" )

df_survey_freq %>%
  dplyr::select( starts_with("freq_nl"), id ) %>% 
  left_join( df_survey_info, by="id" ) %>% 
  filter( country == "The Netherlands" ) %>%
  pivot_longer( cols=starts_with( "freq_nl"), names_to = "specialty", values_to = "consumed" ) %>% 
  calc_perc( specialty, consumed ) %>% 
  plot_freq_specialty( "Frequency of consumption of Dutch specialty products",
                       fill_by=consumed, my_labels=nl_labels )

#ggsave("figures/freq_nl_specialty.png", width = 35, height = 25, units="cm", dpi=300)

1.5.6 Norway

no_labels <- c( "freq_no_lammerull" = "Lammerull",
                "freq_no_sylterull" = "Sylterull",
                "freq_no_kjottrull" = "Kjottrull",
                "freq_no_smalahove" = "Smalahove", 
                "freq_no_whale" = "Whale" )

df_survey_freq %>%
  dplyr::select( starts_with("freq_no"), id ) %>% 
  left_join( df_survey_info, by="id" ) %>% 
  filter( country == "Norway" ) %>%
  pivot_longer( cols=starts_with( "freq_no"), names_to = "specialty", values_to = "consumed" ) %>% 
  calc_perc( specialty, consumed ) %>% 
  plot_freq_specialty( "Frequency of consumption of Norwegian specialty products",
                       fill_by=consumed, my_labels=no_labels )

#ggsave("figures/freq_no_specialty.png", width = 35, height = 25, units="cm", dpi=300)

1.5.7 Spain

es_labels <- c( "freq_es_morcilla"  = "Morcilla",
                "freq_es_lomo_orza" = "Lomo Orza" )

df_survey_freq %>%
  dplyr::select( starts_with("freq_es"), id ) %>% 
  left_join( df_survey_info, by="id" ) %>% 
  filter( country == "Spain" ) %>%
  pivot_longer( cols=starts_with( "freq_es"), names_to = "specialty", values_to = "consumed" ) %>% 
  calc_perc( specialty, consumed ) %>% 
  plot_freq_specialty( "Frequency of consumption of Spanish specialty products",
                       fill_by=consumed, my_labels=es_labels )

#ggsave("figures/freq_es_specialty.png", width = 35, height = 25, units="cm", dpi=300)

2 Types of meat consumed

2.1 Poultry

labels_poultry = c( "type_poultry_chicken" = "Chicken",
                  "type_poultry_turkey"  = "Turkey",
                  "type_poultry_duck"    = "Duck",
                  "type_poultry_goose"   = "Goose", 
                  "type_poultry_ostrich" = "Ostrich",
                  "type_poultry_misc"    = "Misc" )

df_survey_type %>%
  dplyr::select( starts_with("type_poultry"), id ) %>% 
  left_join( df_survey_info, by="id" ) %>% 
  pivot_longer( cols=starts_with( "type_"), names_to = "poultry", values_to = "consumed" ) %>%
  mutate( consumed = ifelse( consumed, "Consumed", "Not consumed") %>% as.factor() ) %>% 
  mutate( consumed = fct_relevel( consumed, "Consumed", "Not consumed")) %>% 
  calc_perc( poultry, consumed ) %>% 
  plot_freq( "Type of poultry consumed", fill_by=consumed,
             wrap_by=poultry, my_labels=labels_poultry )

#ggsave("figures/type_poultry.png", width=20, height=20, units="cm" )

2.2 Lamb and Goat

labels_goat <- c( 
  "type_sheep_lamb"  = "Sheep lamb",
  "type_sheep_adult" = "Sheep adult",
  "type_goat_lamb"   = "Goat lamb",
  "type_goat_adult" = "Goat adult" )

df_survey_type %>%
  dplyr::select( starts_with("type_sheep"), starts_with("type_goat"), id ) %>% 
  left_join( df_survey_info, by="id" ) %>% 
  pivot_longer( cols=starts_with( "type_"), names_to = "livestock", values_to = "consumed" ) %>%
  mutate( consumed = ifelse( consumed, "Consumed", "Not consumed") %>% as.factor() ) %>% 
  mutate( consumed = fct_relevel( consumed, "Consumed", "Not consumed")) %>% 
  calc_perc( livestock, consumed ) %>% 
  plot_freq( "Type of goat or sheep consumed", fill_by=consumed,
             wrap_by=livestock, my_labels=labels_goat )

#ggsave("figures/type_sheep_goat.png", width=20, height=20, units="cm" )

2.3 Other Livestock

labels_livestock <- c( 
  "type_livestock_donkey"   = "Donkey",
  "type_livestock_buffalo"  = "Buffalo",
  "type_livestock_rabbit"   = "Rabbit",
  "type_livestock_reindeer" = "Reindeer",
  "type_livestock_misc"     = "Misc" )

df_survey_type %>%
  dplyr::select( starts_with("type_livestock"), id ) %>% 
  left_join( df_survey_info, by="id" ) %>% 
  pivot_longer( cols=starts_with( "type_"), names_to = "livestock", values_to = "consumed" ) %>%
  mutate( consumed = ifelse( consumed, "Consumed", "Not consumed") %>% as.factor() ) %>% 
  mutate( consumed = fct_relevel( consumed, "Consumed", "Not consumed")) %>% 
  calc_perc( livestock, consumed ) %>% 
  plot_freq( "Type of livestock consumed", fill_by=consumed,
             wrap_by=livestock, my_labels=labels_livestock )

#ggsave("figures/type_livestock.png", width=30, height=20, units="cm" )

2.4 Game

labels_game <- c( 
  "type_game_boar"      = "Boar",
  "type_game_rabbit"    = "Rabbit",
  "type_game_hare"      = "Hare",
  "type_game_reindeer"  = "Reindeer", 
  "type_game_moose"     = "Moose",    
  "type_game_misc_deer" = "Misc. Deer",
  "type_game_mouflon"   = "Mouflon", 
  "type_game_chamois"   = "Chamois",
  "type_game_ibex"      = "Ibex",
  "type_game_misc"      = "Misc." )

df_survey_type %>%
  dplyr::select( starts_with("type_game"), id ) %>% 
  left_join( df_survey_info, by="id" ) %>% 
  pivot_longer( cols=starts_with( "type_"), names_to = "game", values_to = "consumed" ) %>%
  mutate( consumed = ifelse( consumed, "Consumed", "Not consumed") %>% as.factor() ) %>% 
  mutate( consumed = fct_relevel( consumed, "Consumed", "Not consumed")) %>% 
  calc_perc( game, consumed ) %>% 
  plot_freq( "Type of game consumed", fill_by=consumed,
             wrap_by=game, my_labels=labels_game )

#ggsave("figures/type_game.png", width=30, height=20, units="cm" )

2.5 Wild Birds

labels_wildbird <- c( 
  "type_wild_bird_duck"       = "Duck",
  "type_wild_bird_goose"      = "Goose",
  "type_wild_bird_pheasant"   = "Pheasant",
  "type_wild_bird_quail"      = "Quail",
  "type_wild_bird_partridge"  = "Partridge", 
  "type_wild_bird_grouse"     = "Grouse",
  "type_wild_bird_guineafowl" = "Guineafowl",
  "type_wild_bird_pigeon"     = "Pigeon",
  "type_wild_bird_ptarmigan"  = "Ptarmigan",
  "type_wild_bird_misc"       = "Misc." )

df_survey_type %>%
  dplyr::select( starts_with("type_wild"), id ) %>% 
  left_join( df_survey_info, by="id" ) %>% 
  pivot_longer( cols=starts_with( "type_"), names_to = "wildbird", values_to = "consumed" ) %>%
  mutate( consumed = ifelse( consumed, "Consumed", "Not consumed") %>% as.factor() ) %>% 
  mutate( consumed = fct_relevel( consumed, "Consumed", "Not consumed")) %>% 
  calc_perc( wildbird, consumed ) %>% 
  plot_freq( "Type of wild bird consumed", fill_by=consumed,
             wrap_by=wildbird, my_labels=labels_wildbird )

#ggsave("figures/type_wild_birds.png", width=20, height=20, units="cm" )

3 Purchase and Storage Preferences

3.1 Frequency of buying RTE fruit and vegetables

df_survey_process %>%
  dplyr::select( freq_veg_sold_ready, id ) %>% 
  left_join( df_survey_info, by="id" ) %>% 
  calc_perc( freq_veg_sold_ready ) %>% 
  plot_freq(  "Frequency of consuming vegetable and fruit RTE", freq_veg_sold_ready )

#ggsave("figures/freq_veg_ready.png", width=30, height=15, units="cm", dpi=300 )

3.2 Frequency of buying fruit and vegetables from a small scale producer

df_survey_process %>%
  dplyr::select( freq_veg_sold_small_scale, id ) %>% 
  left_join( df_survey_info, by="id" ) %>% 
  calc_perc( freq_veg_sold_small_scale ) %>% 
  plot_freq("Frequency of vegetables bought from small scale producer", freq_veg_sold_small_scale )

#ggsave("figures/freq_veg_small.png", width=30, height=20, units="cm")

3.3 Frequency of buying organic meat

df_survey_process %>%
  dplyr::select( freq_meat_organic, id ) %>% 
  left_join( df_survey_info, by="id" ) %>% 
  calc_perc( freq_meat_organic ) %>% 
  plot_freq(  "Frequency of buying organic meat", freq_meat_organic )

#ggsave("figures/freq_meat_organic.png", width=35, height = 15, units="cm")

3.4 Frequency of buying meat frozen

df_survey_process %>%
  dplyr::select( freq_meat_frozen, id ) %>% 
  left_join( df_survey_info, by="id" ) %>% 
  calc_perc( freq_meat_frozen ) %>% 
  plot_freq( "Frequency of buying meat frozen", freq_meat_frozen )

#ggsave("figures/freq_meat_frozen.png", width=20, height = 15, units="cm")

3.5 Vegetable storage location

store_labels <- c(
  "store_veg_berries"   = "Berries",
  "store_veg_herbs"     = "Herbs",
  "store_veg_leaf"      = "Leafy Greens",
  "store_veg_fermented" = "Fermented vegetables" )

df_survey_process %>%
  dplyr::select( starts_with("store_veg"), id ) %>% 
  left_join( df_survey_info, by="id" ) %>% 
  pivot_longer( cols=starts_with( "store_"), names_to = "prep", values_to = "stored" ) %>% 
  calc_perc( prep, stored ) %>% 
  plot_freq( "Frequency of storing vegetable in fridge or freezer",
             fill_by=stored, wrap_by=prep, my_labels=store_labels )

#ggsave("figures/store_veg.png", width = 30, height = 20, units="cm" )

3.6 Meat storage location

store_meat_labels <- c(
  "store_meat_cut"           = "Meat Cuts",
  "store_meat_mince"         = "Minced Meat",
  "store_meat_sausage_fresh" = "Fresh Sausage",
  "store_meat_spread"        = "Meat Spread",      
  "store_meat_cured"         = "Cured Meat",
  "store_meat_dry_fermented" = "Dry Fermented Sausage",
  "store_meat_dry"           = "Dry Sausage"         
)

df_survey_process %>%
  dplyr::select( starts_with("store_meat"), id ) %>% 
  left_join( df_survey_info, by="id") %>% 
  pivot_longer( cols=starts_with( "store_"), names_to = "prep", values_to = "stored" ) %>% 
  calc_perc( prep, stored ) %>%
  plot_freq( my_title="Frequency of storing meat in fridge or freezer",
             fill_by=stored, wrap_by=prep, my_labels=store_meat_labels )  + 
  guides(fill = guide_legend(nrow = 3, byrow = T))

#ggsave("figures/store_meat.png", height = 25, width = 37, units="cm", dpi=300 )

3.7 Quality of your freezer

df_survey_process %>%
  left_join( df_survey_info, by="id" ) %>% 
  calc_perc( quality_storage_meat ) %>% 
  plot_freq( "Quality of the freezer",
             fill_by=quality_storage_meat, wrap_by=NULL, my_labels=NULL ) +
  scale_fill_manual("legend", values = c(brewer.pal(3, "Blues"),"brown","dark grey","light grey"))+ 
  guides( "", fill = guide_legend(title="", nrow = 3, byrow = T))
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.

#ggsave("figures/process_quality_freezer.png", width = 35, height = 25, units="cm", dpi=300 )

4 Preparation Preferences

4.1 Frequency of washing vegetables

wash_labels= c( 
  "wash_veg_ready"   = "Ready to eat",
  "wash_veg_berries" = "Berries",
  "wash_veg_pome_fr" = "Pomme Fruit",
  "wash_veg_brassic" = "Brassica",
  "wash_veg_cucurbi" = "Cucurbi",   
  "wash_veg_tomato"  = "Tomato",
  "wash_veg_peppers" = "Peppers",
  "wash_veg_fungi"   = "Fungi",
  "wash_veg_herbs"   = "Herbs",
  "wash_veg_leafy_veg" = "Leafy vegetables", 
  "wash_veg_sprouts"   = "Sprouts",
  "wash_veg_leafy_misc" = "Misc",
  "wash_veg_roots"      = "Roots", 
  "wash_veg_stems"      = "Stems" )           

df_survey_process %>%
  dplyr::select( starts_with("wash_veg"), id ) %>% 
  left_join( df_survey_info, by="id" ) %>% 
  pivot_longer( cols=starts_with( "wash_"), names_to = "prep", values_to = "washed" ) %>%
  calc_perc( prep, washed ) %>% 
  plot_freq( "Frequency of washing vegetables",
             wrap_by=prep, fill_by=washed, my_labels=wash_labels )

#ggsave("figures/freq_wash_veg.png", width = 35, height = 25, units="cm" )

5 Consumption Preferences

5.1 Preparation Rare/Medium/Welldone

prep_labels = c( "prep_meat_cuts"    = "Meat Cuts", 
                 "prep_meat_mince"   = "Minced Meat", 
                 "prep_meat_sausage" = "Sausage" )  

df_survey_process %>%
  dplyr::select( starts_with("prep_meat"), id ) %>% 
  left_join( df_survey_info, by="id" ) %>% 
  pivot_longer( cols=starts_with( "prep_"), names_to = "prep", values_to = "cooked" ) %>%
  calc_perc( prep, cooked ) %>% 
  plot_freq( "Frequency of preparation style", cooked,
             wrap_by=prep, my_labels=prep_labels )

#ggsave("figures/prep_style_meat.png", width = 25, height = 15, units="cm", dpi=300 )

5.2 Frequency of eating fruit and vegetables raw

labels_freq_raw_veg <- c(
  "freq_fruit_raw_unpeeled" = "Raw unpeeled fruit",
  "freq_veg_raw_smoothie"   = "Raw vegetable smoothie",
  "freq_veg_raw"            = "Raw vegetables" )

df_survey_process %>%
  dplyr::select( freq_fruit_raw_unpeeled, freq_veg_raw_smoothie, freq_veg_raw, id ) %>%
  pivot_longer( -id, names_to="fruit", values_to="freq" ) %>% 
  left_join( df_survey_info, by="id" ) %>% 
  calc_perc(fruit, freq ) %>% 
  plot_freq("Frequency of eating fruit or vegetables raw", freq,
            wrap_by=fruit, my_labels=labels_freq_raw_veg )

#ggsave("figures/freq_raw_fruit_veg.png", width = 35, height = 25, units="cm", dpi=300 )

5.3 Fermented sausage type prefered

df_survey_type %>%
  dplyr::select( type_fermented_sausage , id ) %>%
  left_join( df_survey_info, by="id" ) %>% 
  filter(!is.na(type_fermented_sausage)) %>%
  calc_perc( type_fermented_sausage ) %>%
  plot_freq(  "Type of fermented sausage preferred", type_fermented_sausage )

#ggsave("figures/type_fermented_sausage.png", width=40, height=20, units="cm" )

5.4 Portion Sizes

5.4.1 Meatball portion size

TODO: insert figures

df_survey_process %>%
  dplyr::select( vol_meat_meatball, id ) %>% 
  left_join( df_survey_info, by="id" ) %>% 
  calc_perc( vol_meat_meatball ) %>% 
  plot_freq(  "Volume of meatball consumed", vol_meat_meatball )

#ggsave("figures/vol_meatball.png", width=30, height = 15, units="cm")

5.4.2 Sausage portionsize

TODO: insert figures

df_survey_process %>%
  dplyr::select( vol_meat_sausage, id ) %>% 
  left_join( df_survey_info, by="id" ) %>% 
  calc_perc( vol_meat_sausage ) %>% 
  plot_freq(  "Volume of sausage consumed", vol_meat_sausage )

#ggsave("figures/vol_sausage.png", width=30, height = 15, units="cm")

5.4.3 Meat cut portion size

TODO: insert figures

df_survey_process %>%
  dplyr::select( vol_meat_cut, id ) %>% 
  left_join( df_survey_info, by="id" ) %>% 
  calc_perc( vol_meat_cut ) %>% 
  plot_freq(  "Volume of meat cuts consumed", vol_meat_cut )

ggsave("figures/vol_meatcut.png", width=30, height = 15, units="cm")

5.4.4 Raw vegetables portion sizes

Note, something was clearly wrong here. Respondents were asked to supply the number of 55 gram portions, but probably supplied the total weight. We have filtered the figure to only show 550g total or less. TODO: insert figures

df_survey_process %>%
  left_join( df_survey_info, by="id" ) %>% 
  filter( vol_veg_raw < 10 ) %>% 
  mutate( vol_veg_raw = vol_veg_raw * 55 ) %>% 
  ggplot( ) +
    geom_histogram( aes(vol_veg_raw, y=..density.., fill=country), 
                    position="dodge", binwidth=20) +
    geom_density( aes(vol_veg_raw, color=country), bw=30 ) +
    scale_x_continuous( "Weight [g]" ) +
    ggtitle( "Portion size raw vegetables" ) +
    my_theme
## Warning: The dot-dot notation (`..density..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(density)` instead.

#ggsave("figures/vol_raw_veg.png", width=25, height = 15, units="cm")

6 Miscelaneous

6.1 Volume of raw meat sampled

When preparing e.g. steak tartare some people may sample the meat for taste.

# TODO: check NA meaning
df_survey_process %>%
  dplyr::select( starts_with("vol_meat_raw"), id ) %>% 
  left_join( df_survey_info, by="id" ) %>% 
  calc_perc( vol_meat_raw_sample ) %>% 
  plot_freq( "Amount of raw meat sampled", vol_meat_raw_sample )

ggsave("figures/raw_meat_sampled.png", width = 20, height = 15, units="cm")